Journal of Applied Mathematics

Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

Zhe-Zhou Yu, Yu-Hao Liu, Bin Li, Shu-Chao Pang, and Cheng-Cheng Jia

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Abstract

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 928051, 10 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305772

Digital Object Identifier
doi:10.1155/2014/928051

Citation

Yu, Zhe-Zhou; Liu, Yu-Hao; Li, Bin; Pang, Shu-Chao; Jia, Cheng-Cheng. Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition. J. Appl. Math. 2014 (2014), Article ID 928051, 10 pages. doi:10.1155/2014/928051. https://projecteuclid.org/euclid.jam/1425305772


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